Cell Culture Control Parameter Generation Using Closed-Loop Modeling
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Solution Overview
Problem
Existing cell cultivation processes face challenges in optimizing process specifications for reactor systems, as they rely on manual data acquisition and lack efficient methods for generating optimized control parameters.
Innovation Solution
An apparatus and method that acquire control variables and targets, derive closed-loop transfer functions, and generate setting parameters to fit control target functions, using a control variable model and control function, to optimize cell culture process control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data acquisition methods are used in cell cultivation processes, then operational simplicity is maintained, but productivity and manufacturing precision are reduced
Solution Approach 1:
The control system automatically acquires cultivation data, derives transfer functions, and generates optimized setting parameters without requiring manual intervention. The system serves itself by autonomously performing data acquisition, mathematical modeling, and parameter optimization, thereby improving productivity while managing complexity through automation.
Solution Approach 2:
Manual data acquisition and parameter adjustment methods are replaced with an automated computational system that uses mathematical modeling and transfer function derivation. This substitution of manual mechanical operations with automated computational processes enables efficient optimization while maintaining manageable system complexity through software-based solutions.
2Manufacturing precision
If automated control parameter generation is implemented, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
A transfer function serves as an intermediary mathematical model that connects the process variables and enables automated derivation of optimized setting parameters. This intermediary modeling approach allows the system to achieve high manufacturing precision by using mathematical relationships as a bridge between process data and control parameters, while keeping the overall system complexity manageable through the use of standardized mathematical tools.
Solution Approach 2:
The system automatically adjusts and optimizes control parameters based on derived transfer functions and process data. By dynamically changing parameters through mathematical optimization rather than manual setting, the system achieves high manufacturing precision while the automation of this process prevents complexity from becoming unmanageable.
3Stability of the object's composition
If closed-loop transfer function derivation is used, then control stability is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary derivation of transfer functions and identification of process characteristics before implementing control. By pre-processing the data and establishing mathematical models in advance, the system can achieve process stability without requiring extremely high measurement precision during operation, as the preliminary modeling phase compensates for measurement variations.
Solution Approach 2:
The closed-loop transfer function incorporates feedback mechanisms that use process measurements to automatically adjust control parameters. This feedback approach improves process stability by continuously adapting to measured variations, while the mathematical modeling framework allows the system to maintain stability even when measurement precision is limited, as the feedback loop compensates for measurement errors.
Data Source
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AI summary
Provided is an apparatus, comprising: an acquisition unit that acquires a control variable and a control target of a control object in a process controlled by a controller; a derivation unit that derives a closed-loop transfer function in a vicinity of an operating point of the process based on a control variable model that represents behavior of the control variable in the process and a control function used in control by the controller; and a setting-parameter generation unit that generates a setting parameter for use in control by the controller so that the closed-loop transfer function fits a control target function that is derived from the control target.